机器学习是什么？AIGC又是什么？机器学习与AIGC未来科技的双引擎

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# 生成数据
np.random.seed(0)
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

# 拟合线性回归模型
model = LinearRegression()
model.fit(X, y)
X_new = np.array([[0], [2]])
y_predict = model.predict(X_new)

# 绘图
plt.scatter(X, y)
plt.plot(X_new, y_predict, color='red', linewidth=2)
plt.title("Linear Regression")
plt.xlabel("X")
plt.ylabel("y")
plt.grid()
plt.show()

from sklearn.linear_model import LogisticRegression

# 生成数据
X = np.random.rand(100, 1) * 10
y = (X > 5).astype(int).ravel()

# 拟合逻辑回归模型
model = LogisticRegression()
model.fit(X, y)

# 绘图
X_test = np.linspace(0, 10, 100).reshape(-1, 1)
y_prob = model.predict_proba(X_test)[:, 1]

plt.scatter(X, y, color='blue')
plt.plot(X_test, y_prob, color='red')
plt.title("Logistic Regression")
plt.xlabel("X")
plt.ylabel("Probability")
plt.grid()
plt.show()

from sklearn import datasets
from sklearn.svm import SVC

# 生成数据
X, y = datasets.make_blobs(n_samples=100, centers=2, random_state=6)
model = SVC(kernel='linear')
model.fit(X, y)

# 绘图
plt.scatter(X[:, 0], X[:, 1], c=y, s=30)
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()

# 创建网格以绘制决策边界
xx = np.linspace(xlim[0], xlim[1], 50)
yy = np.linspace(ylim[0], ylim[1], 50)
YY, XX = np.meshgrid(yy, xx)
xy = np.vstack([XX.ravel(), YY.ravel()]).T
Z = model.decision_function(xy).reshape(XX.shape)

plt.contour(XX, YY, Z, colors='k', levels=[0], alpha=0.5, linestyles='--')
plt.title("Support Vector Machine")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.grid()
plt.show()

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
data = load_iris()
X, y = data.data, data.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建决策树模型
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)

# 预测
y_pred_dt = dt.predict(X_test)

# 评估
accuracy_dt = accuracy_score(y_test, y_pred_dt)
print(f"决策树准确率: {accuracy_dt}")

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载数据集
data = load_iris()
X, y = data.data, data.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建随机森林模型
rf = RandomForestClassifier()
rf.fit(X_train, y_train)

# 预测
y_pred_rf = rf.predict(X_test)

# 评估
accuracy_rf = accuracy_score(y_test, y_pred_rf)
print(f"随机森林准确率: {accuracy_rf}")